Abstract
Increased urbanization and increased observed precipitation intensity and -frequency due to climate change call for urban hydrological models capable of describing urban flow dynamics in data-scarce areas. A parameter parsimonious rainfall-runoff model, DDDUrban, forced by precipitation and temperature in which most model parameters are estimated from a detailed digital elevation model using GIS or taken from the literature is presented. Snowmelt and evapotranspiration are calculated using an energy balance approach, with proxy models for the energy balance elements driven by temperature and precipitation. The model focusses on subsurface and surface flow processes using an analysis of travel time distributions which indicates that the shape of the urban hydrograph is largely independent of the comparatively very rapid process of water transport in conduits. The model uses an estimate of the distribution of subsurface velocities as a function of saturation. The study shows that the calibrated mean of this distribution agrees with the saturated hydraulic conductivity estimated from infiltration measurements. The model has been calibrated and validated on observed runoff data at a 10 min temporal resolution for two Norwegian catchments in Oslo and Trondheim with acceptable validation results measured by the Kling-Gupta Efficiency criterion (KGE = 0.56–0.69). Simulations show that precipitation infiltrated on permeable areas contributes, on average, to the total flow at a fraction corresponding to the areal fraction of permeable areas. In addition, simulations show that for saturated conditions, a significant part (~30–60%) of the flood peak is derived from saturation excess overland flow. Simulation of snowmelt indicates that a more detailed model for the spatial distribution of snow accounting for snow removal, is needed. The catchment-scale effects of Low Impact Developments in the form of 10 m2 raingardens are simulated. In a residential area with 500 houses, 60 raingardens can reduce the flood peaks about 10%. A higher number of raingardens further reduce the flood peaks, but raingardens of too low capacity may increase secondary flood peaks for episodes of multiple heavy precipitation events.
Highlights
Understanding the hydrological response to intense rainfall in urban areas is of paramount importance, as both increased levels of urbanisation (Fletcher et al, 2013; Salvadore et al, 2015) and higher observed short-term rainfall intensities in recent years (Hanssen-Bauer et al, 2017) exert increased pressure on urban runoff systems
DDDurban is coded in Julia and the Kling Gupta efficiency criterion (KGE; Gupta et al, 2009; Kling et al, 2012) was optimized using the global optimization package BlackBoxoptim
As stated in the introduction a tool for assessing urban hydrology at data-scarce sites is needed, and this poses problems that are similar to those encountered when predicting runoff at ungauged sites (see Blӧschl et al, 2013 on theory for prediction in ungauged basins (PUB)). In this respect it is interesting to study the sensitivity of the two parameters calibrated for DDDUrban in this study and evaluate the errors if these parameters were to be estimated without model calibration
Summary
Understanding the hydrological response to intense rainfall in urban areas is of paramount importance, as both increased levels of urbanisation (Fletcher et al, 2013; Salvadore et al, 2015) and higher observed short-term rainfall intensities in recent years (Hanssen-Bauer et al, 2017) exert increased pressure on urban runoff systems. In many urban areas, the only available runoff data are discharge series from combined (CSS) sewer systems in which both wastewater and stormwater flows are combined, leading to a high degree of uncertainty when these data are used for hydrological analyses. Many green solutions for coping with increased stormflow by implementing Low Impact Developments (LIDs), such as green roofs and rain gardens, represent strategies that are of an explicitly hydrological character. Their effectiveness and cost benefit can only be assessed to a reasonable degree of certainty if their potential role in mitigating the urban response to intense rainfall can be reliably quantified. There is, a clear need for robust tools for modelling the urban hydrological regime that can be applied under conditions of limited data availability
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